Accelerometry-based activity monitoring systems are getting more and more widely used. The applications spread over medical and healthcare, rehabilitation, pharmacology and consumer lifestyle domains. For instance, in patient monitoring, the activity sensor provides contextual information that helps to improve the evaluation accuracy of patients' vital body signs, such as ECG and EMG signals; in consumer lifestyle domain, an activity monitor (AM) enables the estimation of energy expenditure (EE) associated with physical activities (PAs) as well as the identification and classification of PAs. Compared to other methods such as video recording, electromyography and questionnaires, accelerometry provides a tool suitable for objective, reliable, long term and low cost monitoring of free-living subjects, with very limited restrictions on their daily lives.
An AM system typically consists of a (number of) triaxial accelerometer(s) and a data logging unit. Sometimes gyroscopes and/or magnetometers are also present. When an accelerometer stays still, it measures the earth gravity g that is decomposed along its three sensing axes. The readout vector can be denoted asVg=(xg,yg,zg).  (1)
If the accelerometer starts to move, besides gravitational acceleration, also inertial acceleration is recorded that results from the movement. Movement of the sensor can be caused by the active movement of the body part to which the sensor is attached as well as the “passive” movement due to external forces. Passive movements can occur, for example, when traveling in a vehicle, cycling on a bumpy road or working with a mowing machine. The readout Va of a moving accelerometer can be therefore described as:Va=Vg+Vact+Vpas,Vact=(xact,yact,zact),Vpas=(xpas,ypas,zpas),  (2)where Vact denotes the acceleration from the active movement, and Vpas the acceleration from the passive movement.
The presence of Vg has been proven to have no significant impact on the accuracy of predicting activity related EE (AEE) [1], but apparently the external motion factors induced acceleration Vpas should not be taken into account. Unlike Vg that is in general the DC of the signal Va and may be quite well estimated simply by low-pass filtering Va, Vpas is often mixed up with Vact in time domain usually as well as in frequency domain, so direct filtering does not always work.
In some special cases, the contribution of Vact to Va is negligible. For instance, when the subject is sitting in or driving a car, a waist-mounted accelerometer measures mostly the acceleration generated by the car, which can come from the road roughness, the motor vibration and/or the speed change, and Vact is almost zero in this case. Thus, if the activity can be correctly recognized as driving, then the acceleration data corresponding to this activity may be excluded from the AEE calculation.
In WO2004/052202A1, a user interface was proposed for an activity monitor that allowed the input from the user indicating the occurrence of “fake” events, such as driving, where the device mainly records external motions, so that a correction factor can be applied in AEE calculation. Also, an automatic scheme based on a single- or multiple-sensor system was described where those events could be recognized by an activity classification algorithm using the readout of the sensor(s). In U.S. Pat. No. 6,280,409B1 and US2002/0116080A1, similarly, a set of thresholding methods was introduced. With these methods, in the former case, the undesired impact of driving on the assessment of the level of activities of daily living (ADL) was largely reduced, and in the latter case, false alarms were avoided in an accelerometry-based system that was capable of judging whether a person would need assistance by monitoring his/her movement changes and could be falsely triggered when he/she was traveling in a vehicle.
There are some obvious drawbacks in the abovementioned methods. Manual fake event indicator requires user intervention, which in practice may lead to inconveniences as well as inaccurate duration information of the events. The reliability of automatic activity recognition methods (of which thresholding methods are a subset) still remain questionable in terms of their robustness over various users and under non-laboratory environments, even though they show acceptable performances on test data sets usually of limited size. More importantly, a common shortcoming is that these methods work only in the case where Vact≈0 and Vpas is dominant, such as traveling in a vehicle. They are not able to tackle situations in which Vact and Vpas are comparable in magnitude or Vpas has a non-negligible effect though Vact is dominant. A typical example would be cycling on a very rough road.